Optimization strategy using dynamic radial basis function metamodel based on trust region

被引：0
|
作者：
机构：
[1] Key Laboratory of Dynamics and Control of Flight Vehicle of Ministry of Education, Beijing Institute of Technology
[2] School of Aerospace Engineering, Beijing Institute of Technology
来源：
Long, T. (tenglong@bit.edu.cn) | 1600年 / Chinese Mechanical Engineering Society卷 / 50期
关键词：
Dynamic metamodel; Multidisciplinary design optimization; Radial basis function; Trust region;
D O I：
10.3901/JME.2014.07.184
中图分类号：
学科分类号：
摘要：
To improve the design quality and optimization efficiency of complicated engineering systems such as flight vehicle, metamodel based optimization is applied widely. Trust region is imported into the metamodel optimization and the updating strategy for sampling space using trust region is proposed and then the optimization strategy using dynamic radial basis function metamodel based on trust region is proposed. The metamodel is constructed with radial basis function and initial sampling points selected by Maximin latin hypercube design method. Global optimization algorithm is employed to optimize current metamodel to find the potential global optimum of the true optimization problem. According to the current known information, the trust region sampling space is updated. During optimization process, the new sampling points in the trust region sampling space are added, and the metamodel is updated, until the potential global optimum of the true optimization problem is satisfied the convergence conditions. The optimization strategy is validated by using five benchmark numerical test problems and an I-beam design problem. As the optimization results shown, the capability of TR-DRBF in both aspects of optimization efficiency and global convergency is good compared with the study fruit inland and overseas at present. Especially for high dimension problems, the performance of TR-DRBF is appealing. © 2014 Journal of Mechanical Engineering.
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页码：184 / 190
页数：6
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